AWS OpenSearch vs. ElasticSearch on EKS: Which One’s Right for Your Team?

When you have a large volume of data, it’s essential to have a search engine that not only finds what you need but also makes sense of your workload. The decision often comes down to choosing between AWS OpenSearch—a fully managed solution—and running ElasticSearch on EKS for full control. Both options have their own strengths and tradeoffs in terms of cost, features, and the level of management required.

In this article, I break down the details, including pricing, engineer time, and advanced features. I also cover when you might consider using spot instances or even Serverless OpenSearch so you can make an informed decision based on your unique needs.

AWS OpenSearch: A Managed and Simpler Option

AWS OpenSearch is designed to offer a hands-off experience. With AWS managing scaling, patching, and backups, you can focus on building and fine-tuning your search capabilities. The simplicity makes it very appealing if you want to get up and running quickly without the overhead of infrastructure management.

However, the convenience comes with some limitations. While it integrates seamlessly with other AWS services, you might miss out on some advanced features required for complex search workloads.

Key benefits of AWS OpenSearch include:

• Quick setup with automatic scaling and seamless AWS integration.

• Minimal operational overhead, letting you focus on core functionality.

On the flip side, it lacks built-in Application Performance Monitoring (APM) and mature machine learning capabilities, and its ecosystem is smaller compared to other options. For example, if you’re running a 3-node cluster continuously with 300 GB of data, you might expect costs like about $1,028 per month for compute, $30 per month for storage, and around $150 per month for engineer time—bringing you to roughly $1,208 per month.

ElasticSearch on EKS: Full Control and Advanced Features

ElasticSearch on EKS gives you complete control over your search engine. This option offers access to the full Elastic Stack—including APM, advanced machine learning, and robust data pipelines. It’s ideal if your team is comfortable managing Kubernetes and you need granular control over every aspect of your search infrastructure.

The advantages of this approach are significant. With full control, you can fine-tune node configurations, scaling policies, and even custom plugins to match your workload. Real-time monitoring through APM allows you to quickly pinpoint performance issues, though the increased control does come with higher operational overhead.

Key advantages of ElasticSearch on EKS include:

• Full access to the Elastic Stack and comprehensive customization.

• Detailed real-time monitoring to quickly identify and resolve issues.

For a similar 3-node setup, your costs might break down to around $1,028 per month for compute, an additional $72 per month for the EKS control plane, roughly $30 per month for storage, and about $600 per month for 8 hours of engineer time—totaling around $1,730 per month.

ElasticSearch on EKS with Spot Instances: Cutting Costs Without Sacrificing Features

Using spot instances on EKS can be a smart move if you need all the advanced features but want to reduce costs. Spot instances can dramatically lower your compute expenses—sometimes by up to 90% compared to on-demand pricing—if you can tolerate occasional interruptions.

This approach allows you to retain full access to the Elastic Stack while accepting some risk of instance reclamation. It’s ideal if your workload can handle short-term disruptions without impacting overall performance.

Key points when using spot instances include:

• Significant cost savings on compute without losing essential features.

• A viable solution if your system is designed to handle occasional interruptions.

For a 3-node setup with spot pricing, your compute costs might drop to around $414 per month, with similar costs for the EKS control plane and storage, plus the same engineer time expense—bringing the total to about $1,116 per month, which is roughly $600 less than the on-demand pricing.

Serverless OpenSearch: Flexibility with a Caveat

Serverless OpenSearch from AWS offers a fully managed, flexible solution that scales automatically based on usage. This model is particularly well-suited for bursty, unpredictable workloads since you only pay for what you use. It eliminates the need for manual scaling and capacity planning, making it a very hands-off option.

However, if your workload is consistently high, costs can escalate quickly. While it works great for sporadic usage, steady high-traffic environments might find it less cost-effective. It’s crucial to monitor your usage closely to understand the cost implications as your data volume or query load increases.

Key highlights of Serverless OpenSearch include:

• Fully managed infrastructure with automatic scaling, ideal for variable workloads.

• Cost efficiency for bursty usage patterns, with the caveat of potentially higher costs under constant heavy load.

For example, with 2 TB of data ingestion and 1,000 hours of query time per month, you might see costs around $204 for ingestion, $450 for query processing, and about $48 for storage, with minimal engineer time adding roughly $75—totaling around $777 per month.

Final Thoughts: Which Option Is Right for You?

Ultimately, the choice depends on your specific requirements. If you value simplicity and seamless integration with other AWS services, AWS OpenSearch is an excellent choice—even if it means sacrificing some advanced features. On the other hand, if you require the full power of the Elastic Stack and are comfortable managing your own infrastructure, ElasticSearch on EKS offers comprehensive control and customization.

For those who want to keep costs down without losing critical functionality, using spot instances with ElasticSearch on EKS is a compelling alternative. And if your workload is unpredictable, Serverless OpenSearch might be the right solution—just keep an eye on potential cost escalations.

I hope this detailed breakdown helps you make an informed decision about your search engine needs. If you have any questions or need further details, feel free to reach out through the comments below. Happy searching!